Abstract
In this paper, a new method is presented for fuzzy inference-based classification. Its base idea lies in the dimensional decomposition of the problem space: the proposed method builds a structure from the training data in which the search among the fuzzy rules is done dimension by dimension, and thus, the number of rules that are needed to be evaluated is gradually restricted. The structure has a layered architecture, where each layer corresponds to a given dimension of the input data and contains a set of fuzzy membership functions, each with a self-balancing binary search tree to quickly identify the relevant fuzzy sets. These are implemented using indexing arrays to enhance the operating speed.
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Acknowledgement
Supported by the ÚNKP-23-4-II-OE-59 New National Excellence Program of the Ministry for Culture and Innovation from the source of the National Research, Development and Innovation Fund. This publication is also the result of the Research & Innovation Operational Programme for the Project: “Support of research and development activities of J. Selye University in the field of Digital Slovakia and creative industry”, ITMS code: NFP313010T504, co-funded by the European Regional Development Fund.
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Tusor, B., Takáč, O., Gubo, Š., Várkonyi-Kóczy, A.R. (2024). Fuzzy Inference with Sequential Fuzzy Indexed Search Trees. In: Ono, Y., Kondoh, J. (eds) Recent Advances in Technology Research and Education. Inter-Academia 2023. Lecture Notes in Networks and Systems, vol 939. Springer, Cham. https://doi.org/10.1007/978-3-031-54450-7_33
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DOI: https://doi.org/10.1007/978-3-031-54450-7_33
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